Natural hazards engineering plays an important role in minimizing the effects of natural hazards on society through the design of resilient and sustainable infrastructure. The DesignSafe cyberinfrastructure has been developed to enable and facilitate transformative research in natural hazards engineering, which necessarily spans across multiple disciplines and can take advantage of advancements in computation, experimentation, and data analysis. DesignSafe allows researchers to more effectively share and find data using cloud services, perform numerical simulations using high performance computing, and integrate diverse datasets so that researchers can make discoveries that were previously unattainable. This paper describes the design principles used in the cyberinfrastructure development process, introduces the main components of the DesignSafe cyberinfrastructure, and illustrates the use of the DesignSafe cyberinfrastructure in research in natural hazards engineering through various examples.

We introduce the UT Campus Object Dataset (CODa), a mobile robot egocentric perception dataset collected on the University of Texas Austin Campus. Our dataset contains 8.5 hours of multimodal sensor data from 3D LiDAR, stereo RGB and RGBD cameras, and a 9-DOF IMU. CODa contains 58 minutes of ground-truth annotations containing 1.3 million 3D bounding boxes with instance IDs for 53 semantic classes, 5000 frames of 3D semantic annotations for urban terrain, and pseudo-ground truth localization. We repeatedly traverse identical geographic regions for diverse indoor and outdoor areas, weather conditions, and times of the day. Using CODa, we empirically demonstrate that: 1) 3D object detection performance improves in urban settings when trained using CODa compared to existing datasets, 2) sensor-specific fine-tuning increases 3D object detection accuracy and 3) pretraining on CODa improves cross-dataset 3D object detection performance in urban settings compared to pretraining on AV datasets. We release benchmarks for 3D object detection and 3D semantic segmentation, with future plans for additional tasks. We publicly release CODa on the Texas Data Repository [1], pre-trained models, dataset development package, and interactive dataset viewer1. We expect CODa to be a valuable dataset for egocentric perception and planning for navigation in urban environments.

We present a case of archival analysis using a combination of data mining methods. The team of researchers, composed by archivists and computer scientists, used a collection of declassified Department of State Cables as a case study. The methods implemented included Support Vector Machine (SVM) and Association Rule Mining. Combined in an analysis workflow, the results of the different methods allowed the team to identify the different security classes, understand how they changed over time and generate descriptions for the cables in each class. The interpretation of results also allowed understanding contextual aspects of the collection. Until now, the use of data mining for archival analysis and processing has not been thoroughly explored by the archival community. This study constitutes a seminal roadmap to understand how to apply, interpret and integrate data mining with the archivists experience and judgment in collaboration with computer scientists. It proposes an inductive approach to archives analysis and the possibility of verifying processing decisions.

The Identifier Services (IDS) project conducted research into and built a prototype to manage distributed genomics datasets remotely and over time. Inspired by archival concepts, IDS allows researchers to track dataset evolution through multiple copies, modifications, and derivatives, independent of where data are located – both symbolically, in the research lifecycle, and physically, in a repository or storage facility. The prototype implementation is based on a three-step data modeling process involving: a) understanding and recording of different researcher workflows, b) mapping the workflows and data to a generic data model and identifying functions, and c) integrating the data model as architecture and interactive functions into cyberinfrastructure (CI). Identity functions are operationalized as continuous tracking of authenticity attributes including data location, differences between seemingly identical datasets, metadata, data integrity, and the roles of different types of local and global identifiers used during the research lifecycle. CI resources were used to conduct identity functions at scale, including scheduling content comparison tasks on high-performance computing resources. The prototype was developed and evaluated considering six data test cases, and feedback was received through a focus-group activity. While there are some technical roadblocks to overcome, our project demonstrates that identity functions are innovative solutions to manage large distributed genomic datasets. The Identifier Services (IDS) project conducted research into and built a prototype to manage distributed genomics datasets remotely and over time. Inspired by archival concepts, IDS allows researchers to track dataset evolution through multiple copies, modifications, and derivatives, independent of where data are located – both symbolically, in the research lifecycle, and physically, in a repository or storage facility. The prototype implementation is based on a three-step data modeling process involving: a) understanding and recording of different researcher workflows, b) mapping the workflows and data to a generic data model and identifying functions, and c) integrating the data model as architecture and interactive functions into cyberinfrastructure (CI). Identity functions are operationalized as continuous tracking of authenticity attributes including data location, differences between seemingly identical datasets, metadata, data integrity, and the roles of different types of local and global identifiers used during the research lifecycle. CI resources were used to conduct identity functions at scale, including scheduling content comparison tasks on high-performance computing resources. The prototype was developed and evaluated considering six data test cases, and feedback was received through a focus-group activity. While there are some technical roadblocks to overcome, our project demonstrates that identity functions are innovative solutions to manage large distributed genomic datasets.

Data driven applications often require using data integrated from different, large, and continuously updated collections. Each of these collections may present gaps, overlapping data, have conflicting information, or complement each other. Thus, a curation need is to continuously assess if data from multiple collections are fit for integration and reuse. To assess different large data collections at the same time, we present the Synchronic Curation (SC) framework. SC involves processing steps to map the different collections to a unifying data model that represents research problems in a scientific area. The data model, which includes the collections' provenance and a data dictionary, is implemented in a graph database where collections are continuously ingested and can be queried. SC has a collection analysis and comparison module to track updates, and to identify gaps, changes, and irregularities within and across collections. Assessment results can be accessed interactively through a web-based interactive graph. In this paper we introduce SC as an interdisciplinary enterprise, and illustrate its capabilities through its implementation in ASTRIAGraph, a space sustainability knowledge system.

This research examines cases of several ‘smart cities’ deploying camera technologies, particularly those augmented by AI and video data capture. Constituent groups including technology companies, city government employees, and a variety of citizen groups both directly and indirectly shape the policies for using these systems and the data they produce, presaging how society might deal with the escalating presence of the Internet of Things. The social shaping of technology approach contributes to the research's conceptual foundations while a political economy perspective frames a consideration of the ethical dynamics in play. The study investigates how seven urban communities frame and articulate the values and dangers of such systems operating in networked environments. Using archival data from official documents, the press, local and State ordinances, the study concludes that police interests have been significant drivers of these systems, alongside new environmental and management promises for improved cost savings. Public engagement practices vary, but in general are either nonexistent or anemic. Oversight practices likewise are under-developed. provides a descriptive picture of how cities view and experience this surveillance infrastructure and highlights of the policy problems associated with the city surveillance systems.